Land Cover Classification from Remote Sensing Imagery: Revisiting and Evaluating Classification Accuracy

1. Land Cover Classification from Remote Sensing Imagery: Revisiting and Evaluating Classification Accuracy Ramita Manandhar, Inakwu Odeh Faculty of Agriculture, Food and Natural Resources The University of Sydney, NSW, Australia

2. Significance: Land use and cover over time and space is a fundamental requirement for environmental monitoring. Remote sensing is an attractive source of thematic maps of land cover due to wide availability at various temporal and spatial scales. Classifying remote sensing imageries to obtain land use/land cover information still a challenge. A review of 15 years of peer-reviewed publications on satellite image classification- no demonstrable improvement in classification performance (Wilkinson, 2005). Heavy dependence on the spectral characteristics is the one of the main reasons for poor reliability of classified map (Jensen, 2005). Need to use ancillary data for improving the accuracy of land cover classification.

3. Aim of this paper Test the hypothesis that the use of ancillary data could lead to improvement of land cover classification

4. 2.2. Data sets Landsat 5 TM subscene of path/row 89/83dated 8 June 2005 from the Australian Centre for Remote Sensing (ACRS). Orthorectified aerial photographs of the period 2004-06 Plateau Images, Alstonville, New South Wales. Data currency- sep 04 – Jul 06. Land use map of Singleton covering the study area projected to GDA 1994 from the Department of Natural Resource, NSW

5. 2.3 Preprocessing Procured landsat TM image of 2005 projected to GDA 1994. The orthorectified aerial photographs were mosaicked together for displaying as one sheet. Land use map of DNR was converted to raster and resampled to match the landsat TM projected to GDA 1994.

6. Table 1. Land cover classes delineated for the classification

7. 2.4 Initial land cover classification based on maximum likelihood algorithm Thermal band removed Multiple signatures collected if a land cover has diff spectral signatures at diff areas Classification using MLC Merging of signatures and filtering with 3x3 majority filter Accuracy assessment- stratified random sampling with 5 classes, each with>50 samples Error matrix Overall accuracy, Kappa stat, user’s & prod. accuracy

8. Builtup class modification High builtup areas of initial classification was not changed. The builtup of the rest of the areas were corrected using a logical rule in expert system. The remainder of builtup class of original classification that are not included in final classification are classified based on their NDVI value. If NDVI is < -0.05, then change that to water body If the NDVI value is between -0.05 and 0.15, then convert to vineyard, Otherwise to pasture. 2.5 Post classification refinement using expert system classification

9. 2.5 Post classification refinement using expert system classification (Contd..) Vineyard class modification Misclassified vineyards in the military area were reclassified to “Pasture and scrubland”. The apparent vineyards classified in the western state forest were reclassified into woodland. Vineyards ≥250m is reclassified as “Pasture and scrubland” using a logic rule. [LU map of DNR and the DEM were utilized in the logic rule].

10. 2.5 Post classification refinement using expert system classification (Contd..) Other modifications Apparent vineyard and builtup classified in real mining patches converted to “Mine and quary”. Small patches of “Mine and quary” seen in the non-mining areas were converted to “Water body”. Small areas of olive seen in the high elevation are converted to Woodland. Corrected map was filtered using 3x3 majority filter to reduce speckles. [not applied for water body]. The road and railway lines are added to the Builtup class and the map was finalized. Accuracy assessment was performed and error matrix was derived.

11. 3.1 Results of Initial classification with MLC Traditional approach to classification only distinguishes forest and non forest land cover (Lu et.al. , 2003). Woodland and water bodies have high accuracy even with MLC. Spectral similarity of vineyards and almost bare ground with scanty vegetation of the military area and rocky open woodland of west. Builtup area overestimated. User’s accuracy of Vineyard and Builtup is < 70 %, i.e. pixels classified as Builtup and Vineyard may not actually exist in ground. 3. Results and Discussion

12. Initial classified map from TM2005 with MLC

13. Table 2. Error matrix showing classification accuracy of initial map classified with MLC * WL= Woodland; PS= Pasture and scrubland; VY= Vineyard; BU = Builtup; WB = Water body; MQ = Mine and quarry; OL = Olive

14. Results of Post classification refinement using expert system classification Apparent vineyard observed in the military area and the western state forest have disappeared. Overestimated builtup in low builtup areas were sharply reduced. Apparent vineyard and builtup classified in real mining patches converted to “Mine and quary”, and mining specks in non mine area were removed. Overall accuracy is 85.4% and User’s accuracy for all the land cover classes are above 70 %.

15. Land cover maps for 2005 produced by (a) Initial classification with MLC; (b) Post-classification correction using ancillary data a. Before correction b. After correction

16. Table 3. Error matrix showing classification accuracy of final map after correction using additional data * WL= Woodland; PS= Pasture and scrubland; VY= Vineyard; BU = Builtup; WB = Water body; MQ = Mine and quarry; OL = Olive

17. Conclusions Incorporation of ancillary data with spectral classification is beneficial to improving land cover classification accuracy. Improvement of image classification is a continuous process.

18. Acknowledgements • Keith Emery and Mick Dwyer of DNR, NSW, Australia. • Late Trevor Drayton, well known Hunter valley wine maker. • Dipak Poudyal, Regional manager- Australia and New zealand, Leica Geosystems Geospatial Imaging

19. Thank You

20. DEM of study area Landsat TM5 with 4, 5, 3 band combination

21. NDVI of TM 2005 Textural image of TM 2005-Band 3